A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells
Bibliographic record
Abstract
Abstract The performance of a proton exchange membrane (PEM) fuel cell is determined by many factors, including operating conditions, component specifications, and system design, making it challenging to predict its performance over a wide range of operating conditions. Existing fuel cell models can be complex and computationally demanding or may be over-simplified by neglecting many transport phenomena. Therefore, a high-fidelity and computationally efficient model is urgently needed for the model-based control of fuel cells. In this study, semi-implicit multi-physics numerical models have been established, taking the mass, momentum, reactants, liquid water, membrane water, electrons, ions, and energy in all fuel cell components into account. The developed 1D model is of high fidelity by incorporating the two-phase flow, non-isothermal effect, and convection, and is still computationally efficient. These models are validated against data from an auto manufacturer with good agreements, and the computing efficiency is evaluated on a modest laptop computer. The modeling results suggest that the two-phase flow model exhibits better prediction accuracy than the single-phase flow model when reactants are fully humidified, while under low humidity conditions, the two models present equivalent performance as liquid water does not exist in the fuel cell components. The results also suggest that the maximum convective/diffusive ratio of H2, O2, and vapor mass fluxes can be 12%, 5.3%, and 35%, respectively, which are ignored in most diffusion-dominant models. The developed models are computationally efficient, requiring only 0.56 s and 0.26 s to simulate a steady-state operation of fuel cells for the two- and single-phase flow models, respectively. This implies that the developed models are suitable for the control of PEM fuel cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".